This paper presents a comprehensive economic analysis and cash flow assessment of a hybrid microgrid system integrating photovoltaic (PV) panels, advanced energy storage solutions, and an existing back-up combined heat and power (CHP) units. The project is designed to provide a sustainable, reliable energy solution with a focus on maximizing financial viability over a 15-year period. The analysis evaluates key financial parameters, including capital investment, operational costs, tax incentives, and utility pricing. The results highlight the economic feasibility of the microgrid, demonstrating its potential for significant returns on investment. Visual representations and data models further illustrate the robustness of the proposed energy infrastructure, underscoring its long-term benefits for stakeholders and investors.

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community.

This research sought to assess the enhancement prospects of electric distribution feeders in Sub-Saharan Africa networks for improving electric power supply to end users. Selected feeder’s network was mapped out for element verification and to produce a map of the feeder, which is pertinent for developing single line and impedance diagrams of the network. The map designing was carried out in AutoCAD workspace. The study was aimed at evaluating Sub-Saharan Africa power electric distribution problems and solution proposals for effective application of existing primary electric distribution power supply. Time series load data on the sample distribution feeder were analyzed to evaluate feeder failure rates, mean down times per outage, and availability. The Sub-Saharan Africa electric distribution transformers voltage profiles and loading were computed by employing explicit assessment. Setbacks recorded on the Sub-Saharan Africa networks comprise: unacceptability fragile voltage profile as a result of poor voltage from transmission station. Mitigating these founded problems expect the power generating industry to establish classic, regular and designed sustenance exercises and fast methods to feedback and reduce the rate of failure in the electric system and time of malfunctioning and system interruptions. The computed Sub-Saharan Africa electric distribution network average availability for the year power supplies in the feeder is 0.68, which is far below the world acceptable standard of 0.999. In view of this, the power- producing industry in Sub-Saharan African nations must ensure that voltage coming out from transmission station is of acceptable standard.

Strategic siting and sizing of renewable-dominated distributed generators (DGs) is pivotal to curbing technical losses and safeguarding voltage quality in modern distribution networks. This paper presents a fully scripted, direct Python-PowerFactory framework that eliminates the file-exchange overhead of conventional MATLAB co-simulation and embeds the recently conceived Deep-Sleep Optimizer (DSO) a meta-heuristic that alternates between “deep-sleep” global exploration and “wake” local exploitation. The proposed workflow is evaluated on the IEEE-33 radial feeder and rigorously benchmarked against the Genetic Algorithm (GA) and Teaching–Learning-Based Optimization (TLBO). The multi-objective problem minimizes real-power losses and maximizes the minimum voltage-stability index subject to generator, voltage, and thermal constraints. For fair comparison all solvers use identical population sizes and iteration budgets. DSO reaches steady convergence in fewer than 20 iterations, whereas TLBO and GA require roughly 40 and 70 iterations, respectively. In the single-DG case DSO and TLBO each halve active losses (to ≈91kW, −56%) and raise the weakest node to 0.983pu, outperforming GA’s 0.978pu. With two DGs, DSO yields the lowest loss (55.9kW, −73%) using only 2.72MW of capacity and completes in 528s one third of TLBO’s runtime. The most demanding three-DG scenario underscores DSO’s superiority: losses plummet from 210kW to 28.9kW (−86%), the weakest-bus voltage climbs to 0.998pu, no bus exceeds 1.013pu, and the installed capacity (3.84MW) is 30% lower than GA’s requirement while runtime (666s) is 81 % shorter than TLBO’s. DSO also trims reactive losses by 81 % and produces the flattest voltage profile (standard deviation <0.005 pu) without breaching the 0.95-1.05 pu statutory band. Because both DSO and TLBO are parameter-free, the framework removes the need for heuristic tuning, making it attractive for day-to-day planning. Overall, the results establish DSO as a high-performance, low-maintenance optimization engine that unlocks deeper loss cuts and tighter voltage regulation than established methods at a computational cost compatible with routine distribution-planning studies. Future work will extend the methodology to time-series optimization with stochastic photovoltaic and load profiles, meshed feeders, and full techno-economic assessment.

The dynamic voltage restorer (DVR), an effective custom device, is used for improving power quality, mitigating voltage sags, and enhancing the overall reliability of power distribution systems. It is proffered to enable Sub-Saharan distribution systems operate optimally. For a system to perform optimally, the voltage loss, voltage supply, reliability of supply, phase current/voltage, must meet the statutory requirements, so that the distribution transformers (DTs) and cables are not overloaded. This study involves the effective mitigation of power quality disturbances in power distribution networks in sub-Saharan distribution networks due to poor voltage profile, voltage variation and voltage imbalance, using a very advanced but effective power electronic-based custom power controller known as the DVR. It is normally connected between the sub-Saharan distribution transformer and the customer load along a feeder with a radial arrangement. An innovative new design-model of the DVR is proposed and developed using VSI-PWM based on dq0 controller. Model simulations were carried out using MATLAB/Simulink in Sim Power System toolbox. Results obtained show that utilizing the proposed method reduces the sub-Saharan distribution systems power quality disturbances to the required standards and acceptable limits.

This work investigates the technical, economic and environmental feasibility of four solar – wind off grid hybrid renewable energy system (HRES) models to provide electrification for Okorobo-Ile town in Andoni Local Government Area of River State, Nigeria using the Hybrid Optimization of Multiple Electric Renewables (HOMER) software. In particular, investigation of the possible inclusion of a fuel cell (FC) system is performed. The four considered models are: pv/wind/battery (PWB); pv/wind/battery/gen-set (PWBG), pv/wind/fuel-cell (PWF) and pv/wind/battery/fuel-cell (PWBF). The best cost-effective configuration among the set of systems were examined for the electricity requirement of 677.75 kWh/day primary load with 99.1kW peak load. Results obtained showed that the net present cost (NPC) are $615,664.95, $679,348.17, $778,834.22 and $3,534,850.54 respectively for the PWB, PWBG, PWBF and PWF. The cost of energy (COE) was lowest for the PWB with a value of $$0.158 and highest for the PWF with a value of $0.964. The renewable options—PWBF and PWF have higher long-term costs but offer cleaner emissions. In contrast, options with the Diesel-Powered Generator is cost-effective but has a high environmental impact in terms of greenhouse gas emissions and noise pollution. These emissions include 3,758kg/yr CO2, 23.7kg/yr CO and a total of 32.67kg/yr of unburned hydrocarbons, sulfur dioxide, particulate matter and nitrogen oxides. Based on the results, a stand - alone HRES that consist of 166kW PV panels, 3 wind turbines 29 batteries and 123kW converter is found to be the best configuration for the village, as it leads to minimum net present cost (NPC) and COE. The PWB system offers the best choice for the community by balancing financial considerations with sustainability which is crucial when making energy system choices. Results also show that while hydrogen, FC system and the electrolyzer can be used together with or without batteries, inclusion of the FC system resulted in the high NPC due to their high cost of investment.

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.

This study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total economic cost (TEC) and total annual cost (TAC), this research emphasizes minimizing the loss of power supply probability (LPSP) and levelized cost of energy (LCOE). The M-MOPSO algorithm introduces a dynamic inertia weight, a unique repository update mechanism, and a dominance-based personal best update strategy, which collectively enhance its performance. Comparative analysis with PSO, NSGA-II, MOPSO and hybrid GA-PSO demonstrates that M-MOPSO consistently achieves a lower LPSP, although its LCOE remains higher. The M-MOPSO optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

While maintaining the delicate balance between grid stability and economic productivity, load shedding remains a crucial yet contentious aspect of South Africa's energy landscape. South Africa has been load-shedding since 2007; however, the country experienced the highest amount of load shedding in 2022, where 11 529 GWh was shed. The National Energy Regulator of South Africa (NERSA) has adopted edition 3 of the National Rationalised Specification (NRS) 048, part 9, designed to facilitate the controlled reduction of up to 80% of the base load by rotating across 16 predefined load-shedding stages. While load shedding is beneficial to maintain network stability under grid constraints, it negatively impacts productivity and the economy. This research paper investigated the role of Battery Energy Storage Systems (BESS) in mitigating 360 random load-shedding events per annum, each lasting two hours, and has shown the immense potential of BESS. With an initial investment of R 250 000 000, ten, 4 MWh, Lithium-Ion BESS maintained the continuity of supply during load shedding; however, the cost to do so translated to R 12.61 kWh, which is R 10.92 kWh more when compared to the grid price of electricity. However, this cost is significantly lower than the estimated Cost of Unserved Energy (COUE), which is R 29.05 GVA/kWh, where GVA represents the gross value added to the economy. Comparatively, the BESS system proves to be a more feasible option, offering optimism for a more stable energy supply in the future. These findings highlight the potential of BESS in managing load shedding and provides valuable insights into its cost and effectiveness.

The Global Navigation Satellite Systems (GNSS) receivers either mobile or immobile are susceptible to Signal to Noise Ratio (SNR) losses. The SNR is also known as C/N_0 and is caused by the quantization, processing, filtering and sampling in the GNSS receiver. In this paper, the signal losses in GNSS receivers is analyzed in terms of maintaining the acceptable SNR. This proposed model addresses the digitization of the GNSS signal received from the space and provide the comprehensive analysis of the SNR losses with the presence of the additive Gaussian noise and signal interference. The analysis of the SNR measurements and losses is very crucial for all GNSS applications, either mobile or immobile GNSS receivers. Poor SNR can also mean there is a possibility of the GNSS multipath caused by enormous time delays for the replicated signals.

The increasing demand for electricity and the need for environmentally friendly transportation systems has resulted in the proliferation of solar photovoltaic (PV) generators and electric vehicle (EV) charging within the low voltage (LV) distribution network. This high penetration of PV and EV charging can cause power quality challenges, hence the need for hosting capacity (HC) studies to estimate the maximum allowable connections. Although studies and reviews are abundant on the HC of PV and EV charging available in the literature, there is a lack of reviews on HC studies that cover both PV and EVs together. This paper fills this research gap by providing a detailed review of five commonly used methods for quantifying HC including deterministic, time series, stochastic, optimization, and streamlined methods. This paper comprehensively reviews the HC concept, methods, and tools, covering both PV and EV charging based on a survey of state-of-the-art literature published within the last five years (2017–2022). Voltage magnitude, thermal limit, and loading of lines, cables, and transformers are the main performance indices considered in most HC studies.

This study addresses the sustainability of aviation in the coming decades. In order to establish a modelling approach for the global air transport sector, the prospective contribution of air transport to fuel consumption and environmental impacts over several decades is considered. To achieve ambitious sustainability goals, it is believed that new aeronautical technologies must be developed and air transport operations must be continuously optimised. This should necessitate substantial investments in research and development (R&D) and equipment by aeronautical manufacturers and air transport operators (airline fleets and airport infrastructures), and queries such as what, how much, and when must be answered. The proposed framework permits levels of detail (air transport services, aircraft classes, and technologies) compatible with strategic decision-making aimed at meeting the demand for air transport services while meeting sustainability goals. Once informed, this framework will enable simulation testing of possible coherent solution scenarios or formulation of global optimisation decision-making problems pertaining to R&D investment in civil aeronautics, fleet renewal by air transport operators, and airport modernization.

A palmprint recognition system identifies a person by using palmprint qualities that may or may not be apparent to the naked eye. It has proven to be appropriate for a variety of purposes and applications such as access control, law enforcement and forensic analysis. Although there are several existing palmprint recognition systems in the literature, they are mostly developed based on localized database, thus querying optimality in other locations. Due to the sparsity of publicly accessible palmprint image databases for black people, we built a non-contact palmprint image database containing 12,000 grayscale images captured from 200 black subjects using three different mobile phone cameras with 5-, 8-, and 12-megapixel resolutions respectively. The palmprints images were preprocessed using mean filtering technique, segmentation and alignment, and this study presents the preliminary results with respect to the dataset for the proposed palmprint recognition system.

High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive. Even though camera sensors' performance is constantly improving, artificially enhanced photos and videos created by intelligent image processing algorithms have significantly improved image fidelity in recent years. Single image super-resolution is a class of algorithms that produces a high-resolution image from a given low-resolution image. Since the advent of deep learning a decade ago, this field has made significant strides. This paper presents a comprehensive review of the deep learning assisted single image super-resolution domain including generative adversarial network (GAN) models that discusses the prominent architectures, models used, and their merits and demerits. The reason behind covering the GAN models is that it is been known to perform better than the conventional deep learning methods given the resources and the time. For real-world applications with noise and other issues that can cause low-fidelity super resolution (SR) images, we examine another solution based on GAN model. This GAN model-based technique popularly known as blind super resolution is more resilient. We examined the various super-resolution techniques by varying image scaling factors (i.e., 2x, 3x, 4x) to measure PSNR and SSIM metrics for the different datasets. PSNR across the different datasets covered in the experimental Section shows an average of 14-17 % decrease in the score as we move up the image resolution scale from 2x to 4x. This is observed across all the datasets and for every model mentioned in the experimental Section of the paper. The results also show that blind super-resolution outperforms the conventional deep learning methods and the more complex GAN models. GAN models are complex and preferred when the upscale factor is high, while residual and dense models are recommended for smaller upscaling factors. This paper also discusses the applications of image super-resolution, and finally, the paper is concluded with challenges and future directions.

The landfill on Bisasar Road in eThekwini has reached the end of its useful life and is undergoing closure and rehabilitation. Converting the landfill into a solar PV and battery storage facility could be a viable contributor to South Africa’s energy crisis. However, solar installations in landfills must consider the ground’s stability, slope, and topography since these factors directly influence the installation and power-generating capacity. This study assessed the viability of integrating solar PV and batteries at the Bisasar Road landfill. The land gradient was analysed using aerial imagery and intervals of spatial contour lines. Five portions of the land totaling 168,000 m2 with suitable gradients for installing solar PV and storage have been identified; however, only 27,500 m2 is deemed ready for solar and battery installations. Using the Hybrid Optimization Model for Electric Renewables (HOMER) modelling software, the techno-economic optimization demonstrates that the least net present cost (NPC) option is to build 5 MW of solar PV, which would cost $2,868,617 and provide an annual revenue of $519,798. Installing 15 MW of batteries requires 5400 m2. However, it would cost $4,302,926 and generate an additional revenue stream of $798,088 annually. Combining 5 MW of solar PV and 15 MW of battery storage requires an initial investment of $7,171,543. However, it boosts the annual income by 217% compared to the current revenue generated through the landfill gas to electricity project. All the modelled projects yielded an internal rate of return of more than 20% and a simple payback period of no more than 5 years, deemed favourable for municipal investments.

Recent advancements in computer vision processing need potent tools to create realistic deepfakes. A generative adversarial network (GAN) can fake the captured media streams, such as images, audio, and video, and make them visually fit other environments. So, the dissemination of fake media streams creates havoc in social communities and can destroy the reputation of a person or a community. Moreover, it manipulates public sentiments and opinions toward the person or community. Recent studies have suggested using the convolutional neural network (CNN) as an effective tool to detect deepfakes in the network. But, most techniques cannot capture the inter-frame dissimilarities of the collected media streams. Motivated by this, this paper presents a novel and improved deep-CNN (D-CNN) architecture for deepfake detection with reasonable accuracy and high generalizability. Images from multiple sources are captured to train the model, improving overall generalizability capabilities. The images are re-scaled and fed to the D-CNN model. A binary-cross entropy and Adam optimizer are utilized to improve the learning rate of the D-CNN model. We have considered seven different datasets from the reconstruction challenge with 5000 deepfake images and 10000 real images. The proposed model yields an accuracy of 98.33% in AttGAN, [Facial Attribute Editing by Only Changing What You Want (AttGAN)] 99.33% in GDWCT,[Group-wise deep whitening-and-coloring transformation (GDWCT)] 95.33% in StyleGAN, 94.67% in StyleGAN2, and 99.17% in StarGAN [A GAN capable of learning mappings among multiple domains (StarGAN)] real and deepfake images, that indicates its viability in experimental setups.

The maximum power point tracking (MPPT) method is necessary to track the maximum power point (MPP) of the PV system regardless of weather conditions since the photovoltaic (PV) system output power is reliant on solar irradiance and operational temperature. The standard method for the MPPT controller is the perturbation and observation (P&O) algorithm, but it has oscillation problems at the MPP operating point; improving the MPP tracked by this algorithm has been a key area of research. This paper presents the design and modelling of a Type-1 and Type-2 Fuzzy Logic Controller (FLC) for tracking the MPP of a PV system in Matlab/Simulink for a 150 W PV system. A comparative analysis of the P&O, Type-1 and Type-2 Fuzzy Logic Controllers was conducted under steady and varied solar irradiation conditions. The simulation results indicated the dominance of the Type-2 FLC algorithm in terms of power loss reduction and minimization of distortions at the MPP.

Harmonic components have developed in power systems due to the nonlinear properties of the circuit components utilized in power electronics-based products and their rapid application. Power systems rely on fundamental quantities like sinusoidally varying voltage and current, which oscillate at a frequency of 50 Hz. The standard restrictions of IEEE-519-1992 are utilized as a benchmark in this study. To generate the best output, the total harmonic distortion (THD) should be decreased below the limit even for certain individual harmonic numbers, and reflect the power factor output. Using the results of the simulation and projections for each mitigation strategy, by using this methodology, the THD I can be reduced below the IEEE-519 standard while also providing cost and electrical advantages. Analyzed and modelled is the PV system, which comprises solar panels, a DC-DC converter, a DC-AC inverter, and a nonlinear load. This paper discusses the fundamentals of filter design and PV system components. It also discusses harmonics' causes and effects, as well as ways to the improvement of power factor (PF), and power quality (PQ) and to make sure that our power systems, do protect all the equipment connected to the PV system. A proposed standalone PV system and a Passive LLCL filter were designed to reduce current total harmonic distortion (THD I ) and voltage total harmonic distortion (THDv). The non-linear loads and the secondary side of the inverter are separated by an LLCL passive filtration, and simulation results obtained with MATLAB/Simulink software are consistent with the theoretical study. The values of THD V and THD I decreased from 90.88% to 1.967% and 74.24% to 1.95%, respectively.

This paper presents a fault location protection scheme in a power distribution system. The paper amplifies the importance of condition monitoring in power systems for reliability enhancement of the network. In this paper, a hybrid protection fault locating scheme based on wavelet packet transform (WPT) and support vector regression (SVR) is proposed. The proposed scheme uses the WPT to extract statistical features of the fault and subsequently, the SVR technique is employed to estimate the location of the fault. Furthermore, the proposed WPT-SVR scheme is tested using 1/2 cycles for signal analysis. An Eskom 132 kV power system line is modelled using the Digsilent platform. Thereafter, various types of fault cases are investigated from the modelled network. The fault classification scheme is tested using a machine learning platform WEKA. The results obtained show that the different impedance variations do not affect the efficiency of the scheme.